

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
	import torch

	from transformers.generation import (
	    MaxLengthCriteria,
	    MaxNewTokensCriteria,
	    MaxTimeCriteria,
	    StoppingCriteriaList,
	    validate_stopping_criteria,
	)



@require_torch
class        A__(unittest.TestCase							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> str:
									a_						:						List[Any] 							= 3
									a_						:						str 							= 250

									a_						:						List[Any] 							= ids_tensor((batch_size, length)  ,      _lowercase    )
									a_						:						Tuple 							= torch.ones((batch_size, length)  ,      device=_lowercase  ,      dtype=torch.float    ) / length
									return input_ids, scores





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_							,					a_						:						Optional[Any] 							= self._get_tensors(5    )

									a_						:						Tuple 							= StoppingCriteriaList(
									    [
									        MaxLengthCriteria(max_length=10    ),
									        MaxTimeCriteria(max_time=0.1    ),
									    ]    )

									self.assertFalse(criteria(_lowercase  ,      _lowercase    )    )

									a_							,					a_						:						List[str] 							= self._get_tensors(9    )
									self.assertFalse(criteria(_lowercase  ,      _lowercase    )    )

									a_							,					a_						:						Tuple 							= self._get_tensors(10    )
									self.assertTrue(criteria(_lowercase  ,      _lowercase    )    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						Optional[Any] 							= MaxLengthCriteria(max_length=10    )

									a_							,					a_						:						Any 							= self._get_tensors(5    )
									self.assertFalse(criteria(_lowercase  ,      _lowercase    )    )

									a_							,					a_						:						str 							= self._get_tensors(9    )
									self.assertFalse(criteria(_lowercase  ,      _lowercase    )    )

									a_							,					a_						:						Dict 							= self._get_tensors(10    )
									self.assertTrue(criteria(_lowercase  ,      _lowercase    )    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									a_						:						Tuple 							= MaxNewTokensCriteria(start_length=5  ,      max_new_tokens=5    )

									a_							,					a_						:						int 							= self._get_tensors(5    )
									self.assertFalse(criteria(_lowercase  ,      _lowercase    )    )

									a_							,					a_						:						Tuple 							= self._get_tensors(9    )
									self.assertFalse(criteria(_lowercase  ,      _lowercase    )    )

									a_							,					a_						:						Optional[Any] 							= self._get_tensors(10    )
									self.assertTrue(criteria(_lowercase  ,      _lowercase    )    )

									a_						:						Dict 							= StoppingCriteriaList([criteria]    )
									self.assertEqual(criteria_list.max_length  ,      10    )





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_							,					a_						:						Union[str, Any] 							= self._get_tensors(5    )

									a_						:						Union[str, Any] 							= MaxTimeCriteria(max_time=0.1    )
									self.assertFalse(criteria(_lowercase  ,      _lowercase    )    )

									a_						:						int 							= MaxTimeCriteria(max_time=0.1  ,      initial_timestamp=time.time() - 0.2    )
									self.assertTrue(criteria(_lowercase  ,      _lowercase    )    )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10    )]    )  ,      10    )

									with self.assertWarns(_lowercase    ):
												validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10    )]    )  ,      11    )

									a_						:						Union[str, Any] 							= validate_stopping_criteria(StoppingCriteriaList()  ,      11    )

									self.assertEqual(len(_lowercase    )  ,      1    )





from random import randint
from tempfile import TemporaryFile

import numpy as np


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						List[Any] 							= 0
			if start < end:
						a_						:						Dict 							= randint(a__  ,	a__)
						a_						:						List[str] 							= a[end]
						a_						:						Tuple 							= a[pivot]
						a_						:						Tuple 							= temp

						a_							,					a_						:						List[Any] 							= _in_place_partition(a__  ,	a__  ,	a__)
						count += _in_place_quick_sort(a__  ,	a__  ,	p - 1)
						count += _in_place_quick_sort(a__  ,	p + 1  ,	a__)
			return count


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= 0
			a_						:						Union[str, Any] 							= randint(a__  ,	a__)
			a_						:						Union[str, Any] 							= a[end]
			a_						:						Any 							= a[pivot]
			a_						:						Any 							= temp
			a_						:						int 							= start - 1
			for index in range(a__  ,	a__):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									a_						:						str 							= new_pivot_index + 1
									a_						:						Optional[Any] 							= a[new_pivot_index]
									a_						:						str 							= a[index]
									a_						:						Union[str, Any] 							= temp

			a_						:						Union[str, Any] 							= a[new_pivot_index + 1]
			a_						:						Tuple 							= a[end]
			a_						:						Any 							= temp
			return new_pivot_index + 1, count


__snake_case				:      Union[str, Any]   		=			TemporaryFile()
__snake_case				:      Dict   		=			1_00  # 1000 elements are to be sorted


__snake_case		, __snake_case				:      int   		=			0, 1  # mean and standard deviation
__snake_case				:      Tuple   		=			np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
__snake_case				:      str   		=			np.load(outfile)
__snake_case				:      Dict   		=			len(M) - 1
__snake_case				:      Dict   		=			_in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)



print(z)


